Pre-pro is a fast pre-processor for single-particle cryo-EM by enhancing 2D classification

Chung, Szu-Chi; Lin, Hsin‐Hung; Niu, Po-Yao; Huang, Shih-Hsin; Tu, I-Ping; Chang, Wen Ho

doi:10.1038/s42003-020-01229-0

Cited by 15 publications

(19 citation statements)

References 49 publications

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“…Two‐stage dimension reduction (2SDR) is a fast dimension‐reduction method that attempts to denoise particles in order to boost the performance of 2D classification algorithms. When attached to other classification algorithms like RELION or ISAC, 2SDR makes the classification step faster, improves the yield of particles and increases the class‐average images to obtain better 3D models 124 . In a combined approach to improve the speed while keeping the accuracy of 2D classification, VAE‐GAN uses a spatial variational autoencoder (VAE) and a generative adversarial network (GAN).…”

Section: Ai In Cryo‐em Image Data Processingmentioning

confidence: 99%

“…When attached to other classification algorithms like RELION or ISAC, 2SDR makes the classification step faster, improves the yield of particles and increases the class-average images to obtain better 3D models. 124 In a combined approach to improve the speed while keeping the accuracy of 2D classification, VAE-GAN uses a spatial variational autoencoder (VAE) and a generative adversarial network (GAN). VAE is used to train and fit the data, and regularize losses, while GAN is used to estimate the probability of which models came from true and generated data.…”

Section: D Classificationmentioning

confidence: 99%

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Artificial intelligence advances for de novo molecular structure modeling in cryo‐electron microscopy

Nakamura

Tang

et al. 2021

WIREs Comput Mol Sci

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Cryo‐electron microscopy (cryo‐EM) has become a major experimental technique to determine the structures of large protein complexes and molecular assemblies, as evidenced by the 2017 Nobel Prize. Although cryo‐EM has been drastically improved to generate high‐resolution three‐dimensional maps that contain detailed structural information about macromolecules, the computational methods for using the data to automatically build structure models are lagging far behind. The traditional cryo‐EM model building approach is template‐based homology modeling. Manual de novo modeling is very time‐consuming when no template model is found in the database. In recent years, de novo cryo‐EM modeling using machine learning (ML) and deep learning (DL) has ranked among the top‐performing methods in macromolecular structure modeling. DL‐based de novo cryo‐EM modeling is an important application of artificial intelligence, with impressive results and great potential for the next generation of molecular biomedicine. Accordingly, we systematically review the representative ML/DL‐based de novo cryo‐EM modeling methods. Their significances are discussed from both practical and methodological viewpoints. We also briefly describe the background of cryo‐EM data processing workflow. Overall, this review provides an introductory guide to modern research on artificial intelligence for de novo molecular structure modeling and future directions in this emerging field. This article is categorized under: Structure and Mechanism > Molecular Structures Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Artificial Intelligence/Machine Learning

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Section: Ai In Cryo‐em Image Data Processingmentioning

confidence: 99%

Section: D Classificationmentioning

confidence: 99%

Artificial intelligence advances for de novo molecular structure modeling in cryo‐electron microscopy

Nakamura

Tang

et al. 2021

WIREs Comput Mol Sci

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“…Besides, to further extend to atomic resolution, one must carefully deal with heterogeneity within the image data. When such is concerned, an MRA-based method has the advantage because it can better differentiate subtle structure differences under low SNR compared with the Bayesian approach [7,8].…”

Section: Introductionmentioning

confidence: 99%

Cryo-Ralib - A Modular Library for Accelerating Alignment in CRYO-EM

Chung

Hung

Siao³

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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Thanks to GPU-accelerated processing, cryo-EM has become a rapid structure determination method that permits capture of dynamical structures of molecules in solution, which has been recently demonstrated by the determination of COVID-19 spike protein in March, shortly after its breakout in late January 2020. This rapidity is critical for vaccine development in response to the emerging pandemic. Compared to the Bayesian-based 2D classification widely used in the workflow, the multi-reference alignment (MRA) is less popular. It is time-consuming despite its superior in differentiating structural variations. Interestingly, the Bayesian approach has higher complexity than MRA. We thereby reason that the popularity of Bayesian is gained through GPU acceleration, where a modular acceleration library for MRA is lacking. Here, we introduce a library called Cryo-RALib that expands the functionality of CUDA library used by GPU ISAC. It contains a GPU-accelerated MRA routine for accelerating MRA-based classification algorithms. In addition, we connect the cryo-EM image analysis with the python data science stack to make it easier for users to perform data analysis and visualization. Benchmarking on the TaiWan Computing Cloud (TWCC) shows that our implementation can accelerate the computation by one order of magnitude. The library is available at https://github.com/phonchi/Cryo-RAlib.

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“…Class averaging in single-particle cryo-EM is an important procedure for producing high-quality initial 3D structures and discarding invalid particles or contaminants [22]. It organizes a dataset by grouping together the particles corresponding to the same (or quite similar) projection directions.…”

Section: Introductionmentioning

confidence: 99%

A Fast Image Alignment Approach for 2D Classification of Cryo-EM Images Using Spectral Clustering

Wang

Liu

2021

CIMB

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Three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is a significant technique for recovering the 3D structure of proteins or other biological macromolecules from their two-dimensional (2D) noisy projection images taken from unknown random directions. Class averaging in single-particle cryo-EM is an important procedure for producing high-quality initial 3D structures, where image alignment is a fundamental step. In this paper, an efficient image alignment algorithm using 2D interpolation in the frequency domain of images is proposed to improve the estimation accuracy of alignment parameters of rotation angles and translational shifts between the two projection images, which can obtain subpixel and subangle accuracy. The proposed algorithm firstly uses the Fourier transform of two projection images to calculate a discrete cross-correlation matrix and then performs the 2D interpolation around the maximum value in the cross-correlation matrix. The alignment parameters are directly determined according to the position of the maximum value in the cross-correlation matrix after interpolation. Furthermore, the proposed image alignment algorithm and a spectral clustering algorithm are used to compute class averages for single-particle 3D reconstruction. The proposed image alignment algorithm is firstly tested on a Lena image and two cryo-EM datasets. Results show that the proposed image alignment algorithm can estimate the alignment parameters accurately and efficiently. The proposed method is also used to reconstruct preliminary 3D structures from a simulated cryo-EM dataset and a real cryo-EM dataset and to compare them with RELION. Experimental results show that the proposed method can obtain more high-quality class averages than RELION and can obtain higher reconstruction resolution than RELION even without iteration.

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Pre-pro is a fast pre-processor for single-particle cryo-EM by enhancing 2D classification

Cited by 15 publications

References 49 publications

Artificial intelligence advances for de novo molecular structure modeling in cryo‐electron microscopy

Artificial intelligence advances for de novo molecular structure modeling in cryo‐electron microscopy

Cryo-Ralib - A Modular Library for Accelerating Alignment in CRYO-EM

A Fast Image Alignment Approach for 2D Classification of Cryo-EM Images Using Spectral Clustering

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